Automation of expert knowledgebases (e.g., network troubleshooting, diagnostics, and debugging) is a process by which expert knowledge (which today lives in the brains of programmers, engineers, etc.) is captured in digital knowledgebases and encoded using some form of semantic technology (e.g., Resource Description Framework (RDF), Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), etc.). The system must enable the complex workflows to be translated to machine consumable information in a simple way. The end goal being to allow machine reasoners to execute based on those knowledgebases, thereby mechanizing the manual workflows that are carried out manually by the experts today.
One of the main challenges to the adoption of this solution is the ease by which is these detailed and potentially complex interwoven knowledgebases can be constructed for various workflows or algorithms. Currently, the task of authoring the ontology associated with a knowledgebase requires proficiency in SWRL, working knowledge in RDF/OWL in addition to a general understanding of how Semantic Reasoners evaluate rules and perform inference. Even though SWRL offers a declarative language to define rules, there is a learning curve associated with these technologies. The requirement is to be able to allow the domain experts, who may not necessarily have any programming background, to construct knowledgebases for their workflows and algorithms.